Land use change modelling using a Markov model and remote sensing

Kumar, Sandeep; Radhakrishnan, Nisha; Mathew, Samson

doi:10.1080/19475705.2013.795502

Cited by 172 publications

(111 citation statements)

References 20 publications

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“…It helps to predict future changes on the basis of current data. A Markov model was used in many studies [16,17,19,34]. The accuracy of Markov models were verified in some studies [19,35].…”

Section: Future Prediction Of Lulc Changesmentioning

confidence: 99%

“…For understanding future scenarios of LULC, different statistical and mathematical models were used. Among them, a Markov model was widely utilized [16,17].…”

Section: Introductionmentioning

confidence: 99%

“…For understanding future scenarios of LULC, different statistical and mathematical models were used. Among them, a Markov model was widely utilized [16,17].Some studies were accomplished to conduct LULC changes and performed spatio-temporal analysis by considering historical trends as well as forecast future LULC. A study was completed for Bangladesh to predict LULC during 2010-2030 using formerly developed Dynamics of Land Systems models on the basis of land use maps (30 m resolution) obtained from China Globe Cover and affecting factors of geophysical, climatic, proximity, and socioeconomic.…”

mentioning

confidence: 99%

“…In another study, spatio-temporal variability was obtained during 1998-2006 using Indian Remote Sensing Satellite (IRS) images, and a Markov model was used to predict future spatial distribution of land for 2014 and 2022 for Tiruchirappalli city of India. The results indicated an increase in built-up area and a decrease in waste land [17]. A Land Transformation Model coupled with geographic information systems (GIS) and artificial neural networks was used for forecasting LULC in Michigan's Grand Traverse Bay Watershed in the United States.…”

mentioning

confidence: 99%

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Investigative Spatial Distribution and Modelling of Existing and Future Urban Land Changes and Its Impact on Urbanization and Economy

Akbar

Hassan

Ishaq³

et al. 2019

Remote Sensing

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Land use and land cover (LULC) change analysis is a critical instrument for studying urban growth across the world. Our objectives were to produce historical LULC maps during the 1988–2016 period for spatial and temporal analysis, forecast future LULC until 2040 by using the Markov model, and identify the impact of LULC on urbanization. Two scenes of Landsat-5 TM for 1988 and 2001 and one scene of Landsat-8 OLI for 2016 were processed and used. The Normalized Difference Vegetation Index (NDVI) model with precise class value ranges was applied to produce land cover maps with six classes of water, built-up, barren land, shrub and grassland, sparse vegetation, and dense vegetation. LULC maps for the years of 1988 and 2001 were used to develop an LULC transformation matrix. It was used to drive an LULC transformation probability matrix using a Markov model for future forecasting of LULC in 2014, 2027, and 2040. The accuracy of 2016 LULC classes was estimated by comparing it against Markov modeled classes. It was found that the areas for: (i) water decreased from 1.43% to 0.51%; (ii) built-up increased from 9.58% to 20.80%; (iii) barren land decreased from 29.50% to 13.40%; (iv) shrub and grass land decreased from 30.57% to 21.10%; (v) sparse vegetation increased from 18% to 20.10%; and (vi) dense vegetation increased from 10.57% to 24.10%. The variations in LULC classes could be noticed by 2040 as compared to 1988. This LULC variation revealed that the water could decrease to 5.32 km2 from 25.37 km2; the built-up could increase to 625.16 km2 from 168.29 km2; the barren land could decrease to 137.53 km2 from 514.13 km2; the shrub and grassland could decrease to 297.68 km2 from 539.46 km2; the sparse vegetation could decrease to 297.68 km2 from 539.46 km2; and the dense vegetation could increase to 409.65 km2 from 191.51 km2. The LULC classification accuracy was 90.27% and 95.11% for 1988 and 2001, respectively. The co-efficient of determination (R2) was found to be 0.90 for 2016 LULC classes obtained from Landsat-8 and derived from a Markov model. For District Lahore, the LULC changes could be related to increasing population and intense migration trends, which had progressive impact on infrastructure development, industrial and economic growth, and detrimental effects on water resources.

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Section: Future Prediction Of Lulc Changesmentioning

confidence: 99%

“…For understanding future scenarios of LULC, different statistical and mathematical models were used. Among them, a Markov model was widely utilized [16,17].…”

Section: Introductionmentioning

confidence: 99%

mentioning

confidence: 99%

mentioning

confidence: 99%

See 2 more Smart Citations

Investigative Spatial Distribution and Modelling of Existing and Future Urban Land Changes and Its Impact on Urbanization and Economy

Akbar

Hassan

Ishaq³

et al. 2019

Remote Sensing

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“…The Markov model considers the conversion from one class to another (class transition) (Kumar et al, 2014). Being P the transition probability of the current class in another class next time, the expression is as follows:…”

Section: Ca-markov Modelmentioning

confidence: 99%

Simulation of changes in land-use/land cover on wetlands through CA-Markov model

Simioni¹,

Guasselli²

2019

Rev. Bras. Geog. Fis.

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A B S T R A C TLand use/land cover modeling is extremely important to land planners, since it allows creating projects and scenarios with possible future threats to ecosystems. Thus, the aim of the present study was to apply the CA-Marvok model to simulate the land use and land cover for the year 2030 in the environmental protection area of the Banhado Grande (EPABG), Brazil. Thus, two images Landsat 5 TM and 8 OLI from 2007 and 2017 were classified. The CA-Markov model was applied through the Land Change Modeler (LCM) tool of the TerrSet software. The results show that the soybean class was the one that most grew between 2007 and 2017, going from 5,891 to 33,844 ha. The simulation shows that 45,375 ha of soybeans will be cultivated at EPABG by 2030, replacing mainly floodplain areas and wetlands (WL's). In view of the above, it is concluded that the simulation of land use land cover for 2030 showed that soybean cultivated area will correspond to approximately 33% of land use and land cover in EPABG. Therefore, it is recommended to create the EPABG management plan focused on the maintenance of the biodiversity and wetlands of the EPABG. Simulação das mudanças no uso e cobertura da terra em áreas úmidas através do modelo CA-Markov R E S U M OA modelagem do uso e cobertura da terra é de vital importância para os planejadores do território, pois permite a criação de projetos e cenários com possíveis ameaças futuras aos ecossistemas. Assim, o objetivo deste trabalho é aplicar o modelo CA-Marvok para simular o uso e cobertura da terra para o ano de 2030, na Área de Proteção Ambiental do Banhado Grande (EPABG), Brasil. Para isto, foram classificadas duas imagens Landsat 5 TM e 8 OLI de 2007 e 2017. O modelo de CA-Markov foi aplicado através da ferramenta Land Change Modeler (LCM) do software TerrSet. Os resultados mostram que a classe de soja foi a que mais cresceu entre 2007 e 2017, passando de 5.891 ha para 33.844. A simulação mostra que para 2030 serão cultivados na EPABG 45.375 ha de soja, substituindo principalmente áreas de várzeas e campos úmidos. Em vista disso, conclui-se que a simulação do uso e cobertura da terra para 2030 mostrou que a área cultivada de soja corresponderá à aprximadamente 33% do uso e cobertura da terra na EPABG. Assim, recomendase a criação do plano de manejo da EPABG, com vistas, principalmente, na manutenção da biodiversidade e também das áreas úmidas da EPABG. Palavras-chave: Modelagem, Sensoriamento Remoto, Áreas Úmidas.

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